交叉验证 cross_validation

from sklearn.datasets import load_boston, load_irisfrom sklearn.linear_model import LinearRegressionfrom sklearn.neighbors import KNeighborsClassifierimport numpy as npfrom sklearn import preprocessing #标准化from sklearn.cross_validation import cross_val_scorefrom sklearn.cross_validation import train_test_splita = np.array([[10,2.7,3.6], [-100, 5, -2], [120, 20, 40]],dtype=np.float64)iris1 = load_iris()print(iris1)iris_X = iris1.datairis = preprocessing.scale(iris_X) #标准化print(iris)iris_y = iris1.targetknn = KNeighborsClassifier(n_neighbors=5)#把原数据集划分五块，每次取1／5为test集，取五次，求哪一个更精确scores = cross_val_score(knn, iris_X,iris_y,cv = 5,scoring = 'accuracy') #for classificationloss = -cross_val_score(knn, iris_X,iris_y,cv = 5,scoring = 'mean_squared_error') #for regression回归 与真实值的误差print(scores.mean())